How to Use the Flipt MCP in LlamaIndex

Q: How do I index my Flipt configurations using LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Flipt server endpoint. Use McpToolSpec to load the tools and feed their outputs directly into your vector index pipelines.

Q: Can LlamaIndex update Flipt flags based on vector search results?

Yes, if you grant the agent write permissions, it can call createvariant or createdistribution based on context retrieved from your vector store. This allows your RAG application to adjust flag rules dynamically.

Q: How does the Flipt MCP Server prevent LLM hallucinations in LlamaIndex?

The server forces the agent to call listflags and listrules to retrieve ground truth data before generating any answers. By grounding the agent's context in real API payloads, LlamaIndex avoids making up nonexistent flag names.

Q: Does LlamaIndex store my Flipt API tokens in the vector database?

No, your API tokens retrieved via listtokens are never indexed or sent to your vector store. They are handled in memory by the MCP client to authenticate requests, ensuring your credentials remain private.

Index Flipt flag configurations directly into your LlamaIndex vector store for semantic, hallucination-free retrieval.

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Setup Flipt with LlamaIndex

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Semantic search over Flipt configurations

Your LlamaIndex agent uses `list_flags` and `list_namespaces` to extract your entire feature flag taxonomy and index it for vector search. This MCP Server integration allows your query engine to find active flags without scanning raw database files. Instead of guessing which flag controls a feature, your agent queries the indexed state to find matching keys. This semantic layer prevents hallucinations because the agent works with actual live data from your namespace.

Indexing Flipt rules for LlamaIndex RAG

The `list_rules` and `list_rollouts` tools provide the raw configuration data that LlamaIndex turns into searchable document nodes. By treating flag rules as document sources, your agent can answer complex questions about who sees which feature and why. This setup merges live API data with your static documentation. When a developer asks about a rollout status, the agent pulls the real-time rule definition directly from the index.

Dynamic segment creation in LlamaIndex

Create and manage user segments using `create_constraint` and `list_segments` within your LlamaIndex indexing pipelines. These tools let your agent map vector database metadata directly to Flipt segment conditions. When your RAG pipeline identifies a new user cohort, the agent can instantly generate the corresponding constraint. This keeps your user targeting synchronized with your semantic database categories.

Setup guide

Set up Flipt MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Flipt MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Flipt tools.",
)
response = await agent.run("List recent Flipt data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Flipt. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Flipt MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Flipt server endpoint. Use `McpToolSpec` to load the tools and feed their outputs directly into your vector index pipelines.

Yes, if you grant the agent write permissions, it can call `create_variant` or `create_distribution` based on context retrieved from your vector store. This allows your RAG application to adjust flag rules dynamically.

The server forces the agent to call `list_flags` and `list_rules` to retrieve ground truth data before generating any answers. By grounding the agent's context in real API payloads, LlamaIndex avoids making up nonexistent flag names.

The MCP connection will timeout according to your client settings. In LlamaIndex, you can set fallback handlers so your query engine relies on cached index data if the server fails to respond.

No, your API tokens retrieved via `list_tokens` are never indexed or sent to your vector store. They are handled in memory by the MCP client to authenticate requests, ensuring your credentials remain private.

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